In this video we look at the datasets that are available to us through TensorFlow Datasets (tfds) and how we load them and then doing preprocessing, shuffling, batching, prefetching etc. For the example we load an image dataset (mnist) and a text dataset (imdb) and create simple models to do image classification and sentiment analysis.

New packages/imports we used in the video:
https://anaconda.org/conda-forge/matplotlib (pip install matplotlib)
https://anaconda.org/anaconda/tensorflow-datasets (pip install tensorflow_datasets)

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OUTLINE:
0:00 - Introduction
0:16 - Keras vs TFDS vs tf.data
1:54 - Imports
2:38 - Image Loading with TFDS
12:59 - Text loading with TFDS
28:15 - Results and Ending

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With so many alternatives available, why are neural nets used for Deep Learning? Neural nets excel at complex pattern recognition and they can be trained quickly with GPUs.

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Historically, computers have only been useful for tasks that we can explain with a detailed list of instructions. As such, they tend to fail in applications where the task at hand is fuzzy, such as recognizing patterns. Neural Networks fill this gap in our computational abilities by advancing machine perception – that is, they allow computers to start to making complex judgements about environmental inputs. Most of the recent hype in the field of AI has been due to progress in the application of deep neural networks.

Neural nets tend to be too computationally expensive for data with simple patterns; in such cases you should use a model like Logistic Regression or an SVM. As the pattern complexity increases, neural nets start to outperform other machine learning methods. At the highest levels of pattern complexity – high-resolution images for example – neural nets with a small number of layers will require a number of nodes that grows exponentially with the number of unique patterns. Even then, the net would likely take excessive time to train, or simply would fail to produce accurate results.

Have you ever had this problem in your own machine learning projects? Please comment.

As a result, deep nets are essentially the only practical choice for highly complex patterns such as the human face. The reason is that different parts of the net can detect simpler patterns and then combine them together to detect a more complex pattern. For example, a convolutional net can detect simple features like edges, which can be combined to form facial features like the nose and eyes, which are then combined to form a face (Credit: Andrew Ng). Deep nets can do this accurately – in fact, a deep net from Google beat a human for the first time at pattern recognition.

However, the strength of deep nets is coupled with an important cost – computational power. The resources required to effectively train a deep net were prohibitive in the early years of neural networks. However, thanks to advances in high-performance GPUs of the last decade, this is no longer an issue. Complex nets that once would have taken months to train, now only take days.

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Transformers are the rage nowadays, but how do they work? This video demystifies the novel neural network architecture with step by step explanation and illustrations on how transformers work.

CORRECTIONS:
The sine and cosine functions are actually applied to the embedding dimensions and time steps!

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ChatGPT is a highly advanced language model developed by OpenAI. It is capable of understanding and generating human-like text with great accuracy and fluency. With its powerful natural language processing capabilities, ChatGPT can assist users in a wide range of applications, including language translation, question answering, and text generation. In this video you will learn all you need to know about ChatGPT and why it’s being compared to google.

⏩ Edureka Chat GPT Explained Playlist: http://bit.ly/3HGRy3G

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Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

#chatgpt #whatischatgpt #EdurekaChatGPT #edureka #chatgpttutorial #chatgptexplained #openai #ai

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In this video on What Is Agile, we'll cover every concept you need to know to understand Agile.
In this video, we'll be covering concepts like:
00:00 Introduction
03:52 The Waterfall Model
07:05 What is Agile?
13:03 Agile Principles
22:24 What are the Advantages of Agile?
26: 25 Agile Methodologies
Without further ado, let's jump in and learn about Agile.

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Random Matrix Theory (RMT) is applied to analyze the weight matrices of
Deep Neural Networks (DNNs), including production quality, pre-trained
models and smaller models trained from scratch. Empirical and theoretical
results indicate that the DNN training process itself implements a
form of self-regularization, evident in the empirical spectral density (ESD)
of DNN layer matrices. To understand this, we provide a phenomenology
to identify 5+1 Phases of Training, corresponding to increasing amounts of
implicit self-regularization. For smaller and/or older DNNs, this implicit self-regularization
is like traditional Tikhonov regularization, with a "size scale" separating signal from
noise. For state-of-the-art DNNs, however, we identify a novel form of
heavy-tailed self-regularization, similar to the self-organization seen
in the statistical physics of disordered systems.

To that end, building on the statistical mechanics of generalization,
and applying recent results from RMT, we derive a new VC-like
complexity metric that resembles the familiar product norms, but
is suitable to study average case generalization behavior in real systems.
We then demonstrate its effectiveness by testing how well this new
metric correlates with trends in the reported test accuracies across models
for over 450 pretrained DNNs covering a range of data sets and architectures.

Learn how to build an AI-enabled organization: https://online.stanford.edu/co....urses/xdgt224-buildi

Incorporating AI has become a dominant aspect of digital transformation strategies. And there’s no hotter AI technology right now than ChatGPT. If you’re a business manager or a technical team leader, it’s time to start thinking about generative AI and its potential for your company, beyond having it write your emails.

In this webinar, join Arvind Karunakaran, Assistant Professor at Stanford University and a faculty affiliate of the Institute for Human-Centered Artificial Intelligence (HAI), to learn about the potential of Generative AI and how it might impact your work and your organization.

You Will Learn:
- What capabilities and challenges are emerging for Generative AI in a workplace
- How to cut through the hype and see the potential of this new technology for the future of work
- How you can anticipate the effects of Generative AI on your role and begin to reimagine or even reskill the work of your team or company

#chatgpt

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In this "Data Analyst Interview Questions and Answers" you will learn the latest and top questions asked by companies for Data Analyst Interviews. This Data Analyst Interview Questions & Answers video covers all kinds of questions starting from basic to advanced questions so you can get benefited. We can assure you that if you watch this Data Analyst Interview Preparation video completely you can crack any Data Engineer job interview easily.

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This Live session on 'Data Science For Non Programmers' will help you explore the basics of Data Science and Machine Learning and in the process also tell you how to use different Python Libraries that let you implement Machine Learning Deep Learning, Data Visualization for Data Science and other purposes Non Programmatically.

🔷About Speaker: Mr. Krishna Prasad P is
1. Director Consulting at CGI.
2. Mentor for W2RT cohort as part of Nasscom initiative.
3. Recipient of prestigious “CGI Builder Award” at CGI level & “Sirius Award” at India level.
4. Submitted various white papers on Data related topics in DCAL (IIMB & IISC).
5. Architected and implemented comprehensive Data platforms for leading Banking firms.
6. Excellent experience in managing programs globally across delivery locations involving multiple time-zones.
7. Driving Pre-sales as Solution Architect & Solution Lead involving large deals.
8. Overall 22+ Years of experience with a strong focus on leading high performing teams & managing delivery excellence.
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About the Master's Program

This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles.

- - - - - - - - - - - - - -

Topics Covered in the curriculum:

Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc.

- - - - - - - - - - - - - -


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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") : https://www.edureka.co/python-....programming-certific
This Edureka PyTorch Tutorial video (Blog: https://goo.gl/4zxMfU) will help you in understanding various important basics of PyTorch. It also includes a use-case in which we will create an image classifier that will predict the accuracy of an image data-set using PyTorch.
00:00:00 Introduction
00:00:19 Agenda
00:00:46 AI, Machine Learning and Deep Learning
00:01:36 What is Deep Learning?
00:02:42 Maximized support!
00:02:59 Python Libraries
00:03:18 PyTorch - An Introduction
00:04:35 PyTorch - Origin
00:05:27 PyTorch - Concepts
00:10:55 The NumPy Bridge!
00:14:16 Autograd Module
00:16:19 Creating a Neural Network
00:27:51 Comparison - An overview
00:29:15 Problem Statement
00:29:46 Dataset
00:30:51 Flow Diagram
00:31:33 Training an image Classifier
00:31:54 Loading and Normalizing CIFAR10
00:32:15 Define a Convolution Neural Network
00:32:41 Define a Loss Function and Optimizer
00:33:46 Train the Network
00:34:01 Test the Network on Test Data
00:40:33 Results
00:40:59 Session In A Minute

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Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.